{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "given-exhaust",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2021-03-18T08:36:38.392736Z",
     "iopub.status.busy": "2021-03-18T08:36:38.392251Z",
     "iopub.status.idle": "2021-03-18T08:36:38.395341Z",
     "shell.execute_reply": "2021-03-18T08:36:38.394847Z"
    },
    "papermill": {
     "duration": 0.047444,
     "end_time": "2021-03-18T08:36:38.395456",
     "exception": false,
     "start_time": "2021-03-18T08:36:38.348012",
     "status": "completed"
    },
    "tags": [
     "parameters"
    ]
   },
   "outputs": [],
   "source": [
    "# Parameters\n",
    "dataset = \"CIFAR10\" # CIFAR10, CIFAR100, SVHN\n",
    "v_type = \"mingd\" # mingd, rand\n",
    "root_path = \"\"\n",
    "params_path = \"\"\n",
    "split_index = 500\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "breathing-respondent",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2021-03-18T08:36:38.470206Z",
     "iopub.status.busy": "2021-03-18T08:36:38.469494Z",
     "iopub.status.idle": "2021-03-18T08:36:38.472091Z",
     "shell.execute_reply": "2021-03-18T08:36:38.472654Z"
    },
    "papermill": {
     "duration": 0.039739,
     "end_time": "2021-03-18T08:36:38.472833",
     "exception": false,
     "start_time": "2021-03-18T08:36:38.433094",
     "status": "completed"
    },
    "tags": [
     "injected-parameters"
    ]
   },
   "outputs": [],
   "source": [
    "# Parameters\n",
    "dataset = \"CIFAR100\"\n",
    "v_type = \"mingd\"\n",
    "root_path = \"/home/mohammad/transferability/files\"\n",
    "params_path = \"/home/mohammad/transferability/src\"\n",
    "split_index = 500\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "solar-chess",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2021-03-18T08:36:38.553493Z",
     "iopub.status.busy": "2021-03-18T08:36:38.552881Z",
     "iopub.status.idle": "2021-03-18T08:36:38.557689Z",
     "shell.execute_reply": "2021-03-18T08:36:38.556780Z"
    },
    "papermill": {
     "duration": 0.049165,
     "end_time": "2021-03-18T08:36:38.557885",
     "exception": false,
     "start_time": "2021-03-18T08:36:38.508720",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/markdown": [
       "# CIFAR100 - MinGD results"
      ],
      "text/plain": [
       "<IPython.core.display.Markdown object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "from IPython.display import display, Markdown\n",
    "v_type2disp = dict(mingd=\"MinGD\", rand=\"Blind Walk\")\n",
    "display(Markdown(f\"# {dataset} - {v_type2disp[v_type]} results\"))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "inner-settle",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2021-03-18T08:36:38.631527Z",
     "iopub.status.busy": "2021-03-18T08:36:38.630912Z",
     "iopub.status.idle": "2021-03-18T08:36:39.121026Z",
     "shell.execute_reply": "2021-03-18T08:36:39.121938Z"
    },
    "papermill": {
     "duration": 0.525374,
     "end_time": "2021-03-18T08:36:39.122261",
     "exception": false,
     "start_time": "2021-03-18T08:36:38.596887",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [],
   "source": [
    "import os, sys\n",
    "sys.path.insert(0, params_path)\n",
    "\n",
    "import argparse, params, time\n",
    "import numpy as np\n",
    "import torch\n",
    "import torch.nn as nn\n",
    "import torch.optim as optim\n",
    "from importlib import reload\n",
    "from tqdm.auto import tqdm\n",
    "\n",
    "import random\n",
    "\n",
    "torch.manual_seed(0)\n",
    "np.random.seed(0)\n",
    "random.seed(0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "naked-joining",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2021-03-18T08:36:39.208597Z",
     "iopub.status.busy": "2021-03-18T08:36:39.207893Z",
     "iopub.status.idle": "2021-03-18T08:36:39.805069Z",
     "shell.execute_reply": "2021-03-18T08:36:39.805984Z"
    },
    "papermill": {
     "duration": 0.648297,
     "end_time": "2021-03-18T08:36:39.806302",
     "exception": false,
     "start_time": "2021-03-18T08:36:39.158005",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [],
   "source": [
    "import seaborn as sns\n",
    "import pandas as pd\n",
    "import os"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "vulnerable-niger",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2021-03-18T08:36:39.895230Z",
     "iopub.status.busy": "2021-03-18T08:36:39.894596Z",
     "iopub.status.idle": "2021-03-18T08:36:39.898032Z",
     "shell.execute_reply": "2021-03-18T08:36:39.897497Z"
    },
    "papermill": {
     "duration": 0.047797,
     "end_time": "2021-03-18T08:36:39.898176",
     "exception": false,
     "start_time": "2021-03-18T08:36:39.850379",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [],
   "source": [
    "from scipy.stats import combine_pvalues, ttest_ind_from_stats, ttest_ind\n",
    "from functools import reduce\n",
    "from scipy.stats import hmean"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "perceived-threshold",
   "metadata": {
    "papermill": {
     "duration": 0.040662,
     "end_time": "2021-03-18T08:36:39.981526",
     "exception": false,
     "start_time": "2021-03-18T08:36:39.940864",
     "status": "completed"
    },
    "tags": []
   },
   "source": [
    "## Use the full vectors to train a linear model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "located-wages",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2021-03-18T08:36:40.068871Z",
     "iopub.status.busy": "2021-03-18T08:36:40.068267Z",
     "iopub.status.idle": "2021-03-18T08:36:40.070245Z",
     "shell.execute_reply": "2021-03-18T08:36:40.070760Z"
    },
    "papermill": {
     "duration": 0.04908,
     "end_time": "2021-03-18T08:36:40.070915",
     "exception": false,
     "start_time": "2021-03-18T08:36:40.021835",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [],
   "source": [
    "root = os.path.join(root_path,dataset)\n",
    "model_types = ['zero-shot', 'prune', 'fine-tune', 'extract-label', 'extract-logit', 'distillation', 'teacher']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "tamil-monte",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2021-03-18T08:36:40.164687Z",
     "iopub.status.busy": "2021-03-18T08:36:40.164092Z",
     "iopub.status.idle": "2021-03-18T08:36:40.174336Z",
     "shell.execute_reply": "2021-03-18T08:36:40.173576Z"
    },
    "papermill": {
     "duration": 0.05967,
     "end_time": "2021-03-18T08:36:40.174546",
     "exception": false,
     "start_time": "2021-03-18T08:36:40.114876",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [],
   "source": [
    "if v_type == \"mingd\" and dataset == \"CIFAR100\": v_type = \"topgd\"\n",
    "    \n",
    "names = [\"teacher\",\"distillation\",\"extract-label\",\"extract-logit\",\"zero-shot\",\"fine-tune\",\"pre-act-18\",\"independent\"]\n",
    "trains = {}\n",
    "tests = {}\n",
    "for name in names:\n",
    "    trains[name] = (torch.load(f\"{root}/model_{name}_normalized/train_{v_type}_vulnerability.pt\"))\n",
    "    tests[name] = (torch.load(f\"{root}/model_{name}_normalized/test_{v_type}_vulnerability.pt\"))\n",
    "mean_cifar = trains[\"teacher\"].mean(dim = (0,1))\n",
    "std_cifar = trains[\"teacher\"].std(dim = (0,1))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "russian-peoples",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2021-03-18T08:36:40.272400Z",
     "iopub.status.busy": "2021-03-18T08:36:40.271748Z",
     "iopub.status.idle": "2021-03-18T08:36:40.287140Z",
     "shell.execute_reply": "2021-03-18T08:36:40.286230Z"
    },
    "papermill": {
     "duration": 0.06723,
     "end_time": "2021-03-18T08:36:40.287366",
     "exception": false,
     "start_time": "2021-03-18T08:36:40.220136",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [],
   "source": [
    "if v_type == \"mingd\":\n",
    "    for name in names:\n",
    "        trains[name] = trains[name].sort(dim = 1)[0]\n",
    "        tests[name] = tests[name].sort(dim = 1)[0]\n",
    "        \n",
    "for name in names:\n",
    "    trains[name] = (trains[name]- mean_cifar)/std_cifar\n",
    "    tests[name] = (tests[name]- mean_cifar)/std_cifar\n",
    "    \n",
    "f_num = 30\n",
    "a_num=30\n",
    "\n",
    "trains_n = {}\n",
    "tests_n = {}\n",
    "for name in names:\n",
    "    trains_n[name] = trains[name].T.reshape(1000,f_num)[:,:a_num]\n",
    "    tests_n[name] = tests[name].T.reshape(1000,f_num)[:,:a_num]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "separate-technique",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2021-03-18T08:36:40.378707Z",
     "iopub.status.busy": "2021-03-18T08:36:40.378152Z",
     "iopub.status.idle": "2021-03-18T08:36:40.381321Z",
     "shell.execute_reply": "2021-03-18T08:36:40.381762Z"
    },
    "papermill": {
     "duration": 0.049641,
     "end_time": "2021-03-18T08:36:40.381918",
     "exception": false,
     "start_time": "2021-03-18T08:36:40.332277",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [],
   "source": [
    "n_ex = split_index\n",
    "train = torch.cat((trains_n[\"teacher\"][:n_ex], tests_n[\"teacher\"][:n_ex]), dim = 0)\n",
    "y = torch.cat((torch.zeros(n_ex), torch.ones(n_ex)), dim = 0)\n",
    "\n",
    "rand=torch.randperm(y.shape[0])\n",
    "train = train[rand]\n",
    "y = y[rand]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "european-elements",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2021-03-18T08:36:40.470397Z",
     "iopub.status.busy": "2021-03-18T08:36:40.469843Z",
     "iopub.status.idle": "2021-03-18T08:36:40.474738Z",
     "shell.execute_reply": "2021-03-18T08:36:40.474029Z"
    },
    "papermill": {
     "duration": 0.050528,
     "end_time": "2021-03-18T08:36:40.474896",
     "exception": false,
     "start_time": "2021-03-18T08:36:40.424368",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [],
   "source": [
    "model = nn.Sequential(nn.Linear(a_num,100),nn.ReLU(),nn.Linear(100,1),nn.Tanh())\n",
    "criterion = nn.CrossEntropyLoss()\n",
    "optimizer =torch.optim.SGD(model.parameters(), lr=0.1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "funded-wilson",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2021-03-18T08:36:40.563532Z",
     "iopub.status.busy": "2021-03-18T08:36:40.562917Z",
     "iopub.status.idle": "2021-03-18T08:36:43.892517Z",
     "shell.execute_reply": "2021-03-18T08:36:43.891916Z"
    },
    "papermill": {
     "duration": 3.375599,
     "end_time": "2021-03-18T08:36:43.892660",
     "exception": false,
     "start_time": "2021-03-18T08:36:40.517061",
     "status": "completed"
    },
    "scrolled": true,
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "f7608c20157e45fe9949a78ce2456c98",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "  0%|          | 0/1000 [00:00<?, ?it/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "with tqdm(range(1000)) as pbar:\n",
    "    for epoch in pbar:\n",
    "        optimizer.zero_grad()\n",
    "        inputs = train\n",
    "        outputs = model(inputs)\n",
    "        loss = -1 * ((2*y-1)*(outputs.squeeze(-1))).mean()\n",
    "        loss.backward()\n",
    "        optimizer.step()\n",
    "        pbar.set_description('loss {}'.format(loss.item()))"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "weighted-hindu",
   "metadata": {
    "papermill": {
     "duration": 0.088396,
     "end_time": "2021-03-18T08:36:44.031415",
     "exception": false,
     "start_time": "2021-03-18T08:36:43.943019",
     "status": "completed"
    },
    "tags": []
   },
   "source": [
    "## Dataset Inference"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "framed-invitation",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2021-03-18T08:36:44.143857Z",
     "iopub.status.busy": "2021-03-18T08:36:44.143237Z",
     "iopub.status.idle": "2021-03-18T08:36:44.145013Z",
     "shell.execute_reply": "2021-03-18T08:36:44.145478Z"
    },
    "papermill": {
     "duration": 0.062094,
     "end_time": "2021-03-18T08:36:44.145644",
     "exception": false,
     "start_time": "2021-03-18T08:36:44.083550",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [],
   "source": [
    "def get_p(outputs_train, outputs_test):\n",
    "    pred_test = outputs_test[:,0].detach().cpu().numpy()\n",
    "    pred_train = outputs_train[:,0].detach().cpu().numpy()\n",
    "    tval, pval = ttest_ind(pred_test, pred_train, alternative=\"greater\", equal_var=False)\n",
    "    if pval < 0:\n",
    "        raise Exception(f\"p-value={pval}\")\n",
    "    return pval\n",
    "\n",
    "def get_p_values(num_ex, train, test, k):\n",
    "    total = train.shape[0]\n",
    "    sum_p = 0\n",
    "    p_values = []\n",
    "    positions_list = []\n",
    "    for i in range(k):\n",
    "        positions = torch.randperm(total)[:num_ex]\n",
    "        p_val = get_p(train[positions], test[positions])\n",
    "        positions_list.append(positions)\n",
    "        p_values.append(p_val)\n",
    "    return p_values\n",
    "\n",
    "def get_fischer(num_ex, train, test, k):\n",
    "    p_values = get_p_values(num_ex, train, test, k)\n",
    "    return combine_pvalues(p_values, method=\"mudholkar_george\")[1]\n",
    "\n",
    "def get_max_p_value(num_ex, train, test, k):\n",
    "    p_values = get_p_values(num_ex, train, test, k)\n",
    "    return max(p_values)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "twenty-stations",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2021-03-18T08:36:44.251550Z",
     "iopub.status.busy": "2021-03-18T08:36:44.250159Z",
     "iopub.status.idle": "2021-03-18T08:36:44.262248Z",
     "shell.execute_reply": "2021-03-18T08:36:44.261677Z"
    },
    "papermill": {
     "duration": 0.066996,
     "end_time": "2021-03-18T08:36:44.262384",
     "exception": false,
     "start_time": "2021-03-18T08:36:44.195388",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [],
   "source": [
    "outputs_tr = {}\n",
    "outputs_te = {}\n",
    "for name in names:\n",
    "    outputs_tr[name] = model(trains_n[name])\n",
    "    outputs_te[name] = model(tests_n[name])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "pretty-occurrence",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2021-03-18T08:36:44.339021Z",
     "iopub.status.busy": "2021-03-18T08:36:44.338575Z",
     "iopub.status.idle": "2021-03-18T08:36:44.341395Z",
     "shell.execute_reply": "2021-03-18T08:36:44.340942Z"
    },
    "papermill": {
     "duration": 0.03993,
     "end_time": "2021-03-18T08:36:44.341499",
     "exception": false,
     "start_time": "2021-03-18T08:36:44.301569",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [],
   "source": [
    "import scipy.stats as stats\n",
    "def print_inference(outputs_train, outputs_test):\n",
    "    m1, m2 = outputs_test[:,0].mean(), outputs_train[:,0].mean()\n",
    "    pval = get_p(outputs_train, outputs_test)\n",
    "    print(f\"p-value = {pval} \\t| Mean difference = {m1-m2}\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "australian-manitoba",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2021-03-18T08:36:44.418979Z",
     "iopub.status.busy": "2021-03-18T08:36:44.418435Z",
     "iopub.status.idle": "2021-03-18T08:36:44.420091Z",
     "shell.execute_reply": "2021-03-18T08:36:44.420445Z"
    },
    "papermill": {
     "duration": 0.039714,
     "end_time": "2021-03-18T08:36:44.420574",
     "exception": false,
     "start_time": "2021-03-18T08:36:44.380860",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [],
   "source": [
    "for name in names:\n",
    "    outputs_tr[name], outputs_te[name] = outputs_tr[name][split_index:], outputs_te[name][split_index:]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "failing-helena",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2021-03-18T08:36:44.495070Z",
     "iopub.status.busy": "2021-03-18T08:36:44.494609Z",
     "iopub.status.idle": "2021-03-18T08:36:44.504234Z",
     "shell.execute_reply": "2021-03-18T08:36:44.504819Z"
    },
    "papermill": {
     "duration": 0.047184,
     "end_time": "2021-03-18T08:36:44.505006",
     "exception": false,
     "start_time": "2021-03-18T08:36:44.457822",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "teacher\n",
      "p-value = 3.609825260662546e-143 \t| Mean difference = 1.2129826545715332\n",
      "distillation\n",
      "p-value = 1.77603226590412e-21 \t| Mean difference = 0.3337082266807556\n",
      "extract-label\n",
      "p-value = 5.4932352759015625e-12 \t| Mean difference = 0.25150901079177856\n",
      "extract-logit\n",
      "p-value = 3.7007108971374775e-17 \t| Mean difference = 0.3813532888889313\n",
      "zero-shot\n",
      "p-value = 5.692529790777427e-23 \t| Mean difference = 0.469923198223114\n",
      "fine-tune\n",
      "p-value = 3.418582275221299e-104 \t| Mean difference = 1.0740493535995483\n",
      "pre-act-18\n",
      "p-value = 6.215526418209104e-127 \t| Mean difference = 1.0475435256958008\n",
      "independent\n",
      "p-value = 0.999997161857082 \t| Mean difference = -0.22725370526313782\n"
     ]
    }
   ],
   "source": [
    "for name in names:\n",
    "    print(f\"{name}\")\n",
    "    print_inference(outputs_tr[name], outputs_te[name])"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "blond-payment",
   "metadata": {
    "papermill": {
     "duration": 0.031074,
     "end_time": "2021-03-18T08:36:44.581231",
     "exception": false,
     "start_time": "2021-03-18T08:36:44.550157",
     "status": "completed"
    },
    "tags": []
   },
   "source": [
    "## Main Loop"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "silver-yahoo",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2021-03-18T08:36:44.655746Z",
     "iopub.status.busy": "2021-03-18T08:36:44.655258Z",
     "iopub.status.idle": "2021-03-18T08:36:44.807237Z",
     "shell.execute_reply": "2021-03-18T08:36:44.806327Z"
    },
    "papermill": {
     "duration": 0.194158,
     "end_time": "2021-03-18T08:36:44.807429",
     "exception": false,
     "start_time": "2021-03-18T08:36:44.613271",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [],
   "source": [
    "if not os.path.exists(f\"{dataset}_{v_type}.h5\"):\n",
    "    total_reps = 40\n",
    "    max_m = 45\n",
    "    total_inner_rep = 100\n",
    "\n",
    "    m_list = [x for x in range(2, max_m, 1)]\n",
    "    p_values_all_threat_models_dict = {}\n",
    "\n",
    "    n_pbar = tqdm(names, leave=False)\n",
    "    for name in n_pbar:\n",
    "        p_vals_per_rep_no = {}\n",
    "        r_pbar = tqdm(range(total_reps), leave=False)\n",
    "        for rep_no in r_pbar:\n",
    "            p_values_list = []\n",
    "            for m in m_list:\n",
    "                p_list = get_p_values(m, outputs_tr[name], outputs_te[name], total_inner_rep)\n",
    "                try:\n",
    "                    hm = hmean(p_list)\n",
    "                except:\n",
    "                    hm = 1.0\n",
    "                p_values_list.append(hm)\n",
    "                pbar.set_description(f\"{rep_no: 2d} {name} m={m: 3d}: f{hm: 0.5f}\")\n",
    "            r_pbar.set_description(f\"rep_no: {rep_no+1}/{total_reps}\")\n",
    "            p_vals_per_rep_no[rep_no] = p_values_list\n",
    "        p_values_all_threat_models_dict[name] = p_vals_per_rep_no\n",
    "        \n",
    "    df_list = []\n",
    "    for name, rep_dict in p_values_all_threat_models_dict.items():\n",
    "        df = pd.DataFrame(rep_dict).reset_index().assign(m=lambda r: r.index+2).drop([\"index\"], axis=1)\n",
    "        df_list.append(pd.melt(df,id_vars=[\"m\"], var_name=\"rep_no\", value_name=\"p_value\").assign(threat_model=name))\n",
    "    results_df = pd.concat(df_list)\n",
    "    \n",
    "    results_df.to_hdf(f\"{dataset}_{v_type}.h5\", v_type)\n",
    "    results_df\n",
    "else:\n",
    "    results_df = pd.read_hdf(f\"{dataset}_{v_type}.h5\", v_type)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "selected-hamburg",
   "metadata": {
    "papermill": {
     "duration": 0.031252,
     "end_time": "2021-03-18T08:36:44.883196",
     "exception": false,
     "start_time": "2021-03-18T08:36:44.851944",
     "status": "completed"
    },
    "tags": []
   },
   "source": [
    "#### Plotting"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "previous-clearing",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2021-03-18T08:36:44.956968Z",
     "iopub.status.busy": "2021-03-18T08:36:44.956219Z",
     "iopub.status.idle": "2021-03-18T08:36:44.958334Z",
     "shell.execute_reply": "2021-03-18T08:36:44.958824Z"
    },
    "papermill": {
     "duration": 0.044388,
     "end_time": "2021-03-18T08:36:44.958995",
     "exception": false,
     "start_time": "2021-03-18T08:36:44.914607",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [],
   "source": [
    "import matplotlib as mpl\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "mpl.rcParams['text.latex.preamble'] = r'\\usepackage{times}'\n",
    "\n",
    "plt.rcParams.update({\n",
    "    \"text.usetex\": True,\n",
    "})"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "forbidden-attitude",
   "metadata": {
    "papermill": {
     "duration": 0.032433,
     "end_time": "2021-03-18T08:36:45.034710",
     "exception": false,
     "start_time": "2021-03-18T08:36:45.002277",
     "status": "completed"
    },
    "tags": []
   },
   "source": [
    "#### Renaming"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "id": "engaging-theory",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2021-03-18T08:36:45.101830Z",
     "iopub.status.busy": "2021-03-18T08:36:45.101370Z",
     "iopub.status.idle": "2021-03-18T08:36:45.103418Z",
     "shell.execute_reply": "2021-03-18T08:36:45.103748Z"
    },
    "papermill": {
     "duration": 0.037962,
     "end_time": "2021-03-18T08:36:45.103873",
     "exception": false,
     "start_time": "2021-03-18T08:36:45.065911",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [],
   "source": [
    "rename_dict = {\n",
    "    \"threat_model\": \"Threat Model\", \n",
    "    \"teacher\": \"Source\", \n",
    "    \"distillation\": \"Distillation\", \n",
    "    \"extract-label\": \"Label-Query\", \n",
    "    \"extract-logit\": \"Logit-Query\", \n",
    "    \"zero-shot\": \"Zero-Shot Learning\",\n",
    "    \"fine-tune\": \"Fine-Tuning\",\n",
    "    \"pre-act-18\": \"Diff. Architecture\",\n",
    "    }"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "id": "junior-being",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2021-03-18T08:36:45.176648Z",
     "iopub.status.busy": "2021-03-18T08:36:45.176198Z",
     "iopub.status.idle": "2021-03-18T08:36:45.209972Z",
     "shell.execute_reply": "2021-03-18T08:36:45.209019Z"
    },
    "papermill": {
     "duration": 0.070387,
     "end_time": "2021-03-18T08:36:45.210209",
     "exception": false,
     "start_time": "2021-03-18T08:36:45.139822",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [],
   "source": [
    "plot_df = (results_df.rename(columns=rename_dict).replace(rename_dict)\n",
    "           .query('(m >= 5)')\n",
    "          )"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "id": "hourly-cloud",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2021-03-18T08:36:45.289131Z",
     "iopub.status.busy": "2021-03-18T08:36:45.288664Z",
     "iopub.status.idle": "2021-03-18T08:36:45.291295Z",
     "shell.execute_reply": "2021-03-18T08:36:45.290927Z"
    },
    "papermill": {
     "duration": 0.04265,
     "end_time": "2021-03-18T08:36:45.291394",
     "exception": false,
     "start_time": "2021-03-18T08:36:45.248744",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [],
   "source": [
    "# Sizes\n",
    "SMALL_SIZE = 14\n",
    "MEDIUM_SIZE = SMALL_SIZE + 1\n",
    "BIGGER_SIZE = MEDIUM_SIZE + 1\n",
    "plt.rc('font', size=SMALL_SIZE)          # controls default text sizes\n",
    "plt.rc('axes', titlesize=SMALL_SIZE)     # fontsize of the axes title\n",
    "plt.rc('axes', labelsize=MEDIUM_SIZE)    # fontsi ze of the x and y labels\n",
    "plt.rc('xtick', labelsize=SMALL_SIZE)    # fontsize of the tick labels\n",
    "plt.rc('ytick', labelsize=SMALL_SIZE)    # fontsize of the tick labels\n",
    "plt.rc('legend', fontsize=SMALL_SIZE)    # legend fontsize\n",
    "plt.rc('figure', titlesize=BIGGER_SIZE)  # fontsize of the figure title"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "id": "dirty-vulnerability",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2021-03-18T08:36:45.385485Z",
     "iopub.status.busy": "2021-03-18T08:36:45.384269Z",
     "iopub.status.idle": "2021-03-18T08:36:51.972764Z",
     "shell.execute_reply": "2021-03-18T08:36:51.973620Z"
    },
    "papermill": {
     "duration": 6.644099,
     "end_time": "2021-03-18T08:36:51.973902",
     "exception": false,
     "start_time": "2021-03-18T08:36:45.329803",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0, 0.5, 'p-value')"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1200x450 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, ax = plt.subplots(figsize=(8, 3), dpi=150)\n",
    "g = sns.lineplot(x=\"m\", y=\"p_value\", hue=\"Threat Model\", data=plot_df, ax=ax, ci=99)\n",
    "g.axhline(0.05, ls='--', color=\"black\", alpha=0.4)\n",
    "g.axhline(0.01, ls='--', color=\"black\", alpha=0.4)\n",
    "g.set_xlabel(\"Number of Sample Revealed\")\n",
    "g.set_ylabel(\"p-value\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "amino-microphone",
   "metadata": {
    "papermill": {
     "duration": 0.043721,
     "end_time": "2021-03-18T08:36:52.065187",
     "exception": false,
     "start_time": "2021-03-18T08:36:52.021466",
     "status": "completed"
    },
    "tags": []
   },
   "source": [
    "# Table"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "id": "opposed-inquiry",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2021-03-18T08:36:52.159853Z",
     "iopub.status.busy": "2021-03-18T08:36:52.159311Z",
     "iopub.status.idle": "2021-03-18T08:36:52.533522Z",
     "shell.execute_reply": "2021-03-18T08:36:52.532092Z"
    },
    "papermill": {
     "duration": 0.424484,
     "end_time": "2021-03-18T08:36:52.533703",
     "exception": false,
     "start_time": "2021-03-18T08:36:52.109219",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "cad00609b393475cb39e6187f6946011",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "  0%|          | 0/8 [00:00<?, ?it/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "from utils import generate_table\n",
    "tab = generate_table(selected_m=10, outputs_te=outputs_te, outputs_tr=outputs_tr, names=names)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "id": "dedicated-proceeding",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2021-03-18T08:36:52.611338Z",
     "iopub.status.busy": "2021-03-18T08:36:52.610929Z",
     "iopub.status.idle": "2021-03-18T08:36:52.613955Z",
     "shell.execute_reply": "2021-03-18T08:36:52.613399Z"
    },
    "papermill": {
     "duration": 0.042718,
     "end_time": "2021-03-18T08:36:52.614098",
     "exception": false,
     "start_time": "2021-03-18T08:36:52.571380",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "               mean_diff  p_value\n",
      "teacher            1.219    0.000\n",
      "distillation       0.362    0.017\n",
      "pre-act-18         1.016    0.000\n",
      "zero-shot          0.466    0.015\n",
      "fine-tune          1.047    0.000\n",
      "extract-label      0.270    0.056\n",
      "extract-logit      0.385    0.025\n",
      "independent       -0.242    0.545\n"
     ]
    }
   ],
   "source": [
    "with pd.option_context(\"display.float_format\", lambda x: f\"{x: 0.3f}\"):\n",
    "    print(tab)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "id": "excess-plane",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2021-03-18T08:36:52.722527Z",
     "iopub.status.busy": "2021-03-18T08:36:52.720804Z",
     "iopub.status.idle": "2021-03-18T08:36:52.724460Z",
     "shell.execute_reply": "2021-03-18T08:36:52.724823Z"
    },
    "papermill": {
     "duration": 0.052849,
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      "\\begin{tabular}{lrr}\n",
      "\\toprule\n",
      "{} & mean_diff &    p_value \\\\\n",
      "\\midrule\n",
      "teacher       &     1.219 & $10^{-16}$ \\\\\n",
      "distillation  &     0.362 & $10^{-02}$ \\\\\n",
      "pre-act-18    &     1.016 & $10^{-06}$ \\\\\n",
      "zero-shot     &     0.466 & $10^{-02}$ \\\\\n",
      "fine-tune     &     1.047 & $10^{-07}$ \\\\\n",
      "extract-label &     0.270 & $10^{-02}$ \\\\\n",
      "extract-logit &     0.385 & $10^{-02}$ \\\\\n",
      "independent   &    -0.242 & $10^{-01}$ \\\\\n",
      "\\bottomrule\n",
      "\\end{tabular}\n",
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